MATH:108:02 Introduction to Statistics Spring, 2014
Syllabus
Instructor: Kenneth Brakke
Office: 308 Fisher Hall
Office phone: 4466
Email: brakke@susqu.edu
I will use email to communicate with the class outside of meeting
times, e.g. for homework corrections, hints, etc. So check your
email regularly. When emailing me, be sure to use a good subject
line. Blank subjects, and generic subjects like "question" and "help"
are liable to get eaten by our spam filter. Use a subject like
"Intro Stat homework question".
Office hours: 3:00 - 4:00 MWF, 1:00 - 3:00 TTh officially.
I am usually in my office 8:30 to 5:00 except for lunch and my other
classes (10:00-11:05 MWF and 12:30-1:35 MWF).
You can also make an appointment.
Text: Introductory Statistics by Neil A. Weiss, 9th edition.
Purpose: To learn basic statistics. The three main parts of statistics are
1. Descriptive statistics - summarizing and organizing data;
histograms, averages, variance, percentiles, etc.
2. Probability - predicting the properties of a random sample from a
known population. Probability rules, expected values, standard
deviation, conditional probability, Bayes' Theorem.
3. Statistical inference - inferring the properties of a population
from data about a randomly chosen sample. Sampling, significance,
p-values, hypothesis testing, linear regression.
Grading:
3 Hour exams 50%
Final exam 25%
Daily homework, quizzes 25%
Your final letter grade will be based on your course average, with
the A-B-C-D cutoffs around the traditional 90-80-70-60 marks, but
I may adjust that as I see fit.
Homework: There will be daily homework assignments, to be handed in at the
start of class. Each assignment will be graded. Assignments will
include exercises using the computer statistics program Minitab or
the spreadsheet program Excel. There will also be short in-class
quizzes about every other class. At the end of the semester,
there will be a small project, in which you design, carry out, and
analyze a statistical experiment.
Homework is due daily at the start of class, and will be graded.
Late homework will get half credit, unless previous arrangements
have been made (i.e. tell me when you are going to miss class
for some excellent reason, or email me when you are too sick to
come to class).
Final exam: there will be a comprehensive final exam held in the official
final exam period for this course, 8:00-10:00 Thursday, May 1.
Tell your family not to plan family vacations, weddings, rides
home etc. until after finals.
OVER
General:
Roll will not be taken, but frequent absences will be noticed.
You are still subject to the attendence policy in the Student
Handbook.
Policy on cheating: Don't. Studying together to understand the
material is fine, but the work you hand in is to be your own.
See the Student Handbook statement on academic honesty.
My tests include writing definitions of vocabulary terms. Using
the first sentence from a Wikipedia article as a definition is
NOT a good idea:
a. The article may be talking about a different meaning of the term.
b. The article may use terms you don't know, like "exponentially
bounded".
c. The first sentence may be woefully incomplete as a definition.
The lists of possible terms will be given on review sheets before
the tests, and you should practice writing definitions of the
terms, and THINK about what those definitions mean.
This course satisfies the Central Curriculum Analytical Thought
requirement.
Central Curriculum Analytical Thought learning goals:
1. Abstract a problem into a symbolic or mathematical model
or framework.
Typically, problems in probability and statistical inference
will be posed as word problems, and students will translate
them into mathematical models.
2. Interpret such a model or framework in terms of a real-
world construct.
For a word problem, after solving the relevant mathematical
problem, the mathematical result must be translated back
into the terms of the original problem, in a form
intelligible to an appropriate audience.
3. Reason from precisely stated principles using deductive
methods and draw valid conclusions.
Each type of mathematical model or problem-solving technique has
specific prerequisites for its validity, and specific techniques
for valid solution.
4. Recognize, manipulate and reason from or about abstract
patterns.
The abstract patterns involved here are centered around
probability, randomness, sampling, error, and statistical
inference.